Apply Function to Every Row in pandas DataFrame Using Multiple Column Values as Parameters

Question

Given a pandas DataFrame with multiple columns

pd.DataFrame({'name': ['Bob', 'Alice'], 'age': [20, 40], 'height': [2.0, 2.1]})

    name  age  height
0    Bob   20     2.0
1  Alice   40     2.1

And a function that takes multiple parameters

def example_hash(name: str, age: int) -> str:
    return "In 10 years {} will be {}".format(name, age+10)

How can the DataFrame be updated with an additional column which contains the result of applying a function to a subset of the other columns?

The resulting DataFrame would be the result of applying example_hash to the name & age columns:

    name  age  height                            hash
0    Bob   20     2.0     In 10 years Bob would be 30
1  Alice   40     2.1    In 10 years Alice will be 50

I'm interested in a pandas centric response. I understand that it's possible to construct a python list, iterate over the rows, and append to the list which would eventually become the column.

Thank you in advance for your consideration and response.

Isn't apply what you are looking for? pandas.pydata.org/pandas-docs/stable/reference/api/… — Jason Chia
– Jason Chia, Commented Apr 22, 2020 at 11:20

Mayank Porwal · Accepted Answer · 2020-04-22 11:34:24Z

You can do this without changing your example_hash() method:

Just use `np.vectorize`

In [2204]: import numpy as np 

In [2200]: def example_hash(name: str, age: int) -> str: 
      ...:     return "In 10 years {} will be {}".format(name, age+10) 
      ...:                                    
In [2202]: df['new'] = np.vectorize(example_hash)(df['name'], df['age'])                                                                                                                                    

In [2203]: df                                                                                                                                                                                               
Out[2203]: 
    name  age  height                           new
0    Bob   20     2.0    In 10 years Bob will be 30
1  Alice   40     2.1  In 10 years Alice will be 50

OR use `df.apply` with `lambda` like this without changing your custom method:

In [2207]: df['new'] = df.apply(lambda x: example_hash(x['name'], x['age']), axis=1)                                                                                                                        

In [2208]: df                                                                                                                                                                                               
Out[2208]: 
    name  age  height                           new
0    Bob   20     2.0    In 10 years Bob will be 30
1  Alice   40     2.1  In 10 years Alice will be 50

Rajat Mishra · Accepted Answer · 2020-04-22 11:25:23Z

3

you can use apply function to iterate over the rows and add a new column.

In [139]: df = pd.DataFrame({'name': ['Bob', 'Alice'], 'age': [20, 40], 'height': [2.0, 2.1]})

In [140]: df
Out[140]:
    name  age  height
0    Bob   20     2.0
1  Alice   40     2.1


In [142]: def example_hash(row):
     ...:     row['hash']= "In 10 years {} will be {}".format(row['name'], row['age']+10)
     ...:     return row
     ...:

In [143]: df = df.apply(example_hash,axis=1)

In [144]: df
Out[144]:
    name  age  height                          hash
0    Bob   20     2.0    In 10 years Bob will be 30
1  Alice   40     2.1  In 10 years Alice will be 50

answered Apr 22, 2020 at 11:25

Rajat Mishra

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Apply Function to Every Row in pandas DataFrame Using Multiple Column Values as Parameters

2 Answers 2

Just use `np.vectorize`

OR use `df.apply` with `lambda` like this without changing your custom method:

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2 Answers 2

Just use np.vectorize

OR use df.apply with lambda like this without changing your custom method:

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Just use `np.vectorize`

OR use `df.apply` with `lambda` like this without changing your custom method: